Target-Based, Privacy Preserving, and Incremental Association Rule Mining

Author/Creator ORCID

Date

2015-09-30

Department

Program

Citation of Original Publication

Madhu V. Ahluwalia, Aryya Gangopadhyay, Zhiyuan Chen and Yelena Yesha, Target-Based, Privacy Preserving, and Incremental Association Rule Mining, IEEE Transactions on Services Computing ( Volume: 10 , Issue: 4 ) , 2015, DOI: 10.1109/TSC.2015.2484318

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© 2015 IEEE

Abstract

We consider a special case in association rule mining where mining is conducted by a third party over data located at a central location that is updated from several source locations. The data at the central location is at rest while that flowing in through source locations is in motion. We impose some limitations on the source locations, so that the central target location tracks and privatizes changes and a third party mines the data incrementally. Our results show high efficiency, privacy and accuracy of rules for small to moderate updates in large volumes of data. We believe that the framework we develop is therefore applicable and valuable for securely mining big data.